Publication Name	World Journal of Psychiatry
Manuscript ID	106025
Country	South Korea

Received	2025-02-14 09:22
Peer-Review Started	2025-02-14 09:22
To Make the First Decision
Return for Revision	2025-04-02 11:09
Revised	2025-04-07 08:25
Second Decision	2025-06-11 03:25
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-06-18 08:04
Articles in Press	2025-06-18 08:04
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-07-19 09:39
Publish the Manuscript Online	2025-08-08 10:23

ISSN	2220-3206 (online)
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Category	Psychiatry
Manuscript Type	Minireviews
Article Title	Can reinforcement learning effectively prevent depression relapse?
Manuscript Source	Invited Manuscript
All Author List	Haewon Byeon
ORCID	Author(s) ORCID Number Haewon Byeon http://orcid.org/0000-0002-3363-390X
Funding Agency and Grant Number	Funding Agency Grant Number Education and Research Promotion Program of KOREATECH
Corresponding Author	Haewon Byeon, Associate Professor, PhD, Worker’s Care & Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, No. 1600 Chungjeol-ro, Cheonan 31253, South Korea. bhwpuma@naver.com
Key Words	Reinforcement learning; Depression relapse prevention; Personalized treatment; Machine learning; Mental health interventions
Core Tip	Reinforcement learning (RL) holds significant promise in preventing depression relapse by enabling personalized and adaptive mental health interventions. By leveraging advanced machine learning algorithms, RL can analyze behavioral data for early relapse risk detection and optimize treatment strategies tailored to individual needs. This study reviews the existing literature, highlighting RL’s potential to transform mental health care through personalized learning and data-driven decision-making. However, challenges such as algorithmic complexity and ethical considerations must be addressed. Future research should focus on larger-scale studies and interdisciplinary collaboration to establish RL as a viable tool for effective depression management and relapse prevention.
Publish Date	2025-08-08 10:23
Citation	<p>Byeon H. Can reinforcement learning effectively prevent depression relapse? <i>World J Psychiatry</i> 2025; 15(8): 106025</p>
URL	https://www.wjgnet.com/2220-3206/full/v15/i8/106025.htm
DOI	https://dx.doi.org/10.5498/wjp.v15.i8.106025

Full Article (PDF)	WJP-15-106025-with-cover.pdf
Manuscript File	106025_Auto_Edited_070114.docx
Answering Reviewers	106025-answering-reviewers.pdf
Audio Core Tip	106025-audio.mp3
Conflict-of-Interest Disclosure Form	106025-conflict-of-interest-statement.pdf
Copyright License Agreement	106025-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	106025-foundation-statement.pdf
Non-Native Speakers of English Editing Certificate	106025-non-native-speakers.pdf
Peer-review Report	106025-peer-reviews.pdf
Scientific Misconduct Check	106025-scientific-misconduct-check.png
Scientific Editor Work List	106025-scientific-editor-work-list.pdf
CrossCheck Report	106025-crosscheck-report.pdf